So, with the TOI usage charts I presented the other day you can see how frequently a player was on the ice in any particular situation relative to how frequently the team plays during that situation. So, a player might be on the ice for 30% of the teams 5v5 game tied minutes. The next logical step is to take a look at his production during those situations relative to his teams production. If a player is on the ice for 30% of his teams 5v5 game tied minutes but he was only on the ice for 25% of the teams 5v5 game tied goals, that isn’t a good thing. The team under-produced during his ice time relative to when he was not on the ice. We can also do the same for goals against and the resulting chart might look like this one for Zdeno Chara over the past 5 seasons.

The blue is Chara’s TOI usage percentages, the green is his goals for percentages and the red is his goals against percentages. You will notice that I have removed special teams play. The reason for this is because GA is not significant on power plays and GF is not significant on penalty kill so the chart ends up looking odd but in theory you could include them.

In an ideal situation the red box is smaller than the blue box (give up fewer goals than expected) and the green box is bigger than the blue box (give up more goals than expected). For Chara his results are a little mixed. When trailing he is very good having more goals for than expected and fewer goals against than expected when he is on the ice. His goals against relative to his teammates rises significantly when leading. I am not certain why, but maybe it has to do with his defense pairings when protecting a lead or opposing teams pressure him more when they are trailing.

Let’s take a look at another player who has been in the news lately, for both a contract signing and an injury. Joffrey Lupul.

Strangely, almost the opposite of Chara. Lupul’s ‘leading’ stats are better than Chara’s while Chara is better when trailing. I am thinking maybe matchups are a factor here. When leading coaches are more diligent in matching Chara up against the opposing teams top line and keeping Lupul away from the opposing teams top line. Something to investigate further.

That said though, for Leaf fans if the Leafs get a better team that spends more time leading than trailing, Lupul’s numbers should, at least according to the chart above, get better. Especially goals against numbers.

Let’s finish off with one more superstar player, Sidney Crosby.

That is the chart of an offensively dominant player. Crosby’s offense is through the roof. Like Chara though, he is much weaker protecting a lead than any other situation.

As I said in my previous post, I am not sure where I will go with these radar charts, but they seem to be a valuable way of visualizing data so when appropriate I will attempt to make use of them. For example, it might be interesting to take a look at how a players usage and performance changes from year to year. In particular it might be interesting to see how ice time and performance changes for young players as they slowly improve or older players who are on the downsides of their careers.

One of the challenges in hockey analytics, or any type of data analysis, is how to best visualize data in a way that is exceptionally informative and yet really simple to understand. I have been working on a few things can came up with something that I think might be a useful tool to understand how a player gets utilized by his coach.

Let’s start with some background. We can get an idea of how a player is utilized by looking at when the player gets used and how frequently he gets used. Offensive players get more ice time on the power play and more ice time when their team is trailing and needs a goal. Defensive players get more ice time on the PK and when they are protecting a lead. This all makes sense, but the issue is some teams spend more time on the PP or PK than others while bad teams end up trailing more than good teams and leading less. This means doing a straight time on ice comparison between players on different teams doesn’t always accurately depict the usage of the player. If a player on the Red Wings plays the same number of minutes with the lead as a player on the Blue Jackets it doesn’t mean the players are used int he same way. The Blue Jackets will lead a game significantly less than the Red Wings thus in the hypothetical example above the Blue Jackets are depending on their player a higher percent of the time with a lead than the Red Wings are their player.

To get around this I looked at percentages. If Player A played 500 minutes with a lead and his team played a total of 2000 minutes with a lead during games which Player A played, then Players A’s ice time with a lead percentage would be 25%. In games in which Player A played he was used in 25% of the teams time leading. I can calculated these percentages for any situation from 5v5 to 4v5 or 5v4 special teams to leading and trailing situations. The challenge is to visualize the data in a clear and understandable way. To do this I use radar charts. Lets look at a couple examples so you get an idea and we’ll use players that have extreme and opposite usages: Daniel Sedin and Manny Malhotra.

For those not up to speed on my terminology f10 is zone start adjusted ice time which ignores the 10 seconds after a face off in either the offensive or defensive zone.

The charts above are largely driven by PP and PK ice time but players that are used more often in offensive roles will have their charts bulge to the top and top right while those in more defensive roles will have their charts bulge more to the bottom and bottom left. Also, the larger the ‘polygon’ the more ice time and more relied on the player is. In the examples above, Sedin is clearly used more often in offensive situations and clearly gets more ice time.

Let’s now look at a player who is used in a more balanced way, Zdeno Chara.

That is a chart that is representative of a big ice time player who plays in all situations. We can then take it a step further and compare players such as the following.

In normal 5v5 situations Gardiner was depended on about as much as Phaneuf, but Phaneuf was relied on a lot more on special teams and a bit more when protecting a lead. Of course, you can also compare across teams with these charts:

Phaneuf and Chara were depended on almost equally in all situations except on the PP where Phaneuf was used far more frequently.

I am not sure where I will go with these charts but I think I’ll look at them from time to time as I am sure they will be of use in certain situations and I have a few ideas as to how to expand on them to make them even more interesting/useful.

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Welcome to HockeyAnalysis.com, where I strive to get a better understanding of the game of hockey through the use of statistical analysis. I hope you enjoy whatever time you spend here and maybe even learn a little. If you have any questions or comments, feel free to drop me an e-mail at david (at) hockeyanalysis.com.